이미지의 깊이 정보 분류를 위한 새로운 특징 벡터 추출 방법

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 2. 김태정.Eye fatigue caused by 3D contents having unnatural scene depth level assigned by conventional 2D to 3D conversion system has been issued as one of the worst side effect of 3D contents. For generation of more realistic 3D contents to overcome eye fatigue and other side effects, it is need to study depth control method which has not been studied enough. In this paper, new feature extraction method for image scene depth level classification are introduced. Based on natural phenomenon, we found a direct relation between natural image statistics, and depth pattern, and proposed new texture related features for scene depth level classification. And, to overcome the effect of illumination color on color related feature, we suggested color feature extraction method with illumination color estimation based on conditional color temperature adjustment. Proposed features represent scene depth level efficiently. Finally, proposed features and existed features for indoor-outdoor classification are concatenated to generate the feature vectors and fed into the SVM classifier for the scene depth level classification. To justify the efficiency and robustness of the proposed method, the evaluation is conducted over 600 images.Abstract Contents List of Figures List of Tables Chapter 1 Introduction Chapter 2 Image scene depth classification Chapter 3 Texture related features 3.1 Texture related features in indoor-outdoor classification 3.2 New texture related features for scene depth classification Chapter 4 Color related features 4.1 Illumination color and Color features 4.2 Color correlated temperature and illumination color 4.3 New color related features extraction method Chapter 5 Experiment results Chapter 6 Conclusion References Abstract in KoreanMaste

Sensing Landscape History with an Interactive Location Based Service

This paper introduces the STEAD approach for interpreting data acquired by a “human sensor”, who uses an informal interactive location-based service (iLBS) to sense cultural-historic facts and anecdotes of, and in the landscape. This user-generated data is collected outdoors and in situ. The approach consists of four related facets (who, what, where, when). Three of the four facets are discussed and illustrated by user generated data collected during a Dutch survey in 2008. These data represent the personal cultural-historic knowledge and anecdotes of 150 people using a customized iLBS for experiencing the cultural history of a landscape. The “who” facet shows three dominant mentality groups (cosmopolitans, modern materialists and post modern hedonists) that generated user content. The “what” facet focuses on three subject types of pictures and four picture framing classes. Pictures of the place type showed to be dominant and foreground framing class was slightly favourite. The “where” facet is explored via density, distribution, and distance of the pictures made. The illustrations of the facets indirectly show the role of the “human sensor” with respect to the domain of interest. The STEAD approach needs further development of the when-facet and of the relations between the four facets. Finally the results of the approach may support data archives of iLBS applications

INDOOR-OUTDOOR IMAGE CLASSIFICATION USING DICHROMATIC REFLECTION MODEL AND HARALICK FEATURES

The problem of indoor-outdoor image classification using supervised learning is addressed in this paper. Conventional indoor-outdoor image classification methods, partition an image into predefined sub-blocks for feature extraction. However in this paper, we use a simple color segmentation stage to acquire meaningful regions from the image for feature extraction. The features that are used to describe an image are color correlated temperature, Haralick features, segment area and segment position. For the classification phase, an MLP was trained and tested using a dataset of 800 images. A classification accuracy of 94% compared with the result of other state of the art indoor-outdoor image classification methods showed the efficiency of the proposed method

EFFECTIVE COMBINING OF COLOR AND TEXTURE DESCRIPTORS FOR INDOOR-OUTDOOR IMAGE CLASSIFICATION

Although many indoor-outdoor image classification methods have been proposed in the literature, most of them have omitted comparison with basic methods to justify the need for complex feature extraction and classification procedures. In this paper we propose a relatively simple but highly accurate method for indoor-outdoor image classification, based on combination of carefully engineered MPEG-7 color and texture descriptors. In order to determine the optimal combination of descriptors which is characterized by efficient extraction, compact representation and high accuracy, we conducted comprehensive empirical tests over several color and texture descriptors. The optimal descriptors combination was used for training and testing of a binary SVM classifier. We have shown that the proper descriptors preprocessing before SVM classification has significant impact on the final result. Comprehensive experimental evaluation shows that the proposed method outperforms several more complex indoor-outdoor image classification techniques on a couple of public datasets

A Review of Environmental Context Detection for Navigation Based on Multiple Sensors

Current navigation systems use multi-sensor data to improve the localization accuracy, but often without certitude on the quality of those measurements in certain situations. The context detection will enable us to build an adaptive navigation system to improve the precision and the robustness of its localization solution by anticipating possible degradation in sensor signal quality (GNSS in urban canyons for instance or camera-based navigation in a non-textured environment). That is why context detection is considered the future of navigation systems. Thus, it is important firstly to define this concept of context for navigation and to find a way to extract it from available information. This paper overviews existing GNSS and on-board vision-based solutions of environmental context detection. This review shows that most of the state-of-the art research works focus on only one type of data. It confirms that the main perspective of this problem is to combine different indicators from multiple sensors

The Impact of Edge Enhancement on Nature and Artifact Image Classification

東京電機大学201

Scene Image Classification and Retrieval

Scene image classification and retrieval not only have a great impact on scene image management, but also they can offer immeasurable assistance to other computer vision problems, such as image completion, human activity analysis, object recognition etc. Intuitively scene identification is correlated to recognition of objects or image regions, which prompts the notion to apply local features to scene categorization applications. Even though the adoption of local features in these tasks has yielded promising results, a global perception on scene images is also well-conditioned in cognitive science studies. Since the global description of a scene imposes less computational burden, it is favoured by some scholars despite its less discriminative capacity. Recent studies on global scene descriptors have even yielded classification performance that rivals results obtained by local approaches. The primary objective of this work is to tackle two of the limitations of existing global scene features: representation ineffectiveness and computational complexity. The thesis proposes two global scene features that seek to represent finer scene structures and reduce the dimensionality of feature vectors. Experimental results show that the proposed scene features exceed the performance of existing methods. The thesis is roughly divided into two parts. The first three chapters give an overview on the topic of scene image classification and retrieval methods, with a special attention to the most effective global scene features. In chapter 4, a novel scene descriptor, called ARP-GIST, is proposed and evaluated against the existing methods to show its ability to detect finer scene structures. In chapter 5, a low-dimensional scene feature, GIST-LBP, is proposed. In conjunction with a block ranking approach, the GIST-LBP feature is tested on a standard scene dataset to demonstrate its state-of-the-art performance

Automated inverse-rendering techniques for realistic 3D artefact compositing in 2D photographs

PhD ThesisThe process of acquiring images of a scene and modifying the defining structural features of the scene through the insertion of artefacts is known in literature as compositing. The process can take effect in the 2D domain (where the artefact originates from a 2D image and is inserted into a 2D image), or in the 3D domain (the artefact is defined as a dense 3D triangulated mesh, with textures describing its material properties). Compositing originated as a solution to enhancing, repairing, and more broadly editing photographs and video data alike in the film industry as part of the post-production stage. This is generally thought of as carrying out operations in a 2D domain (a single image with a known width, height, and colour data). The operations involved are sequential and entail separating the foreground from the background (matting), or identifying features from contour (feature matching and segmentation) with the purpose of introducing new data in the original. Since then, compositing techniques have gained more traction in the emerging fields of Mixed Reality (MR), Augmented Reality (AR), robotics and machine vision (scene understanding, scene reconstruction, autonomous navigation). When focusing on the 3D domain, compositing can be translated into a pipeline 1 - the incipient stage acquires the scene data, which then undergoes a number of processing steps aimed at inferring structural properties that ultimately allow for the placement of 3D artefacts anywhere within the scene, rendering a plausible and consistent result with regard to the physical properties of the initial input. This generic approach becomes challenging in the absence of user annotation and labelling of scene geometry, light sources and their respective magnitude and orientation, as well as a clear object segmentation and knowledge of surface properties. A single image, a stereo pair, or even a short image stream may not hold enough information regarding the shape or illumination of the scene, however, increasing the input data will only incur an extensive time penalty which is an established challenge in the field. Recent state-of-the-art methods address the difficulty of inference in the absence of 1In the present document, the term pipeline refers to a software solution formed of stand-alone modules or stages. It implies that the flow of execution runs in a single direction, and that each module has the potential to be used on its own as part of other solutions. Moreover, each module is assumed to take an input set and output data for the following stage, where each module addresses a single type of problem only. data, nonetheless, they do not attempt to solve the challenge of compositing artefacts between existing scene geometry, or cater for the inclusion of new geometry behind complex surface materials such as translucent glass or in front of reflective surfaces. The present work focuses on the compositing in the 3D domain and brings forth a software framework 2 that contributes solutions to a number of challenges encountered in the field, including the ability to render physically-accurate soft shadows in the absence of user annotate scene properties or RGB-D data. Another contribution consists in the timely manner in which the framework achieves a believable result compared to the other compositing methods which rely on offline rendering. The availability of proprietary hardware and user expertise are two of the main factors that are not required in order to achieve a fast and reliable results within the current framework

Approach for reducing the computational cost of environment classification systems for mobile robots

Disertační práce se věnuje problému změny prostředí v úlohách mobilní robotiky. Zaměřuje se na využití jednodimenzionálních nevizuálních senzorů za účelem redukce výpočetních nároků. V práci je představen nový systém pro detekci a klasifikaci prostředí robota založený na datech z kamery a z nevizuálních senzorů. Nevizuální senzory zde slouží jako prostředek detekce probíhající změny, která iniciuje klasifikaci prostředí pomocí kamerových dat. To může významně snížit výpočetní nároky v porovnání se situací, kdy je zpracováván každý a nebo každý n-tý snímek obrazu. Systém je otestován na případu změny prostředí mezi vnitřním a venkovním prostředím. Přínosy této práce jsou následující: (1) Představení systému pro detekci a klasifikaci prostředí mobilního robota; (2) Analýzu state-of-the-art v oblasti Simultánní Lokalizace a Mapování za účelem zjištění otevřených problémů, které je potřeba řešit; (3) Analýza nevizuálních senzorů vzhledem k jejich vhodnosti pro danou úlohu. (4) Analýza existujících metod pro detekci změny ve 2D signálu a představení dvou jednoduchých přístupů k tomuto problému; (5) Analýza state-of-the art v oblasti klasifikace prostředí se zaměřením na klasifikaci vnitřního a venkovního prostředí; (6) Experiment porovnávající metody studované v předchozím bodu. Jedná se dle mých znalostí o nejrozsáhlejší porovnání těchto metod na jednom jediném datasetu. Navíc jsou do experimentu zahrnuty také klasifikátory založené na neuronových sítích, které dosahují lepších výsledků než klasické přístupy; (7) Vytvoření datasetu pro testování navrženého systému na sestaveném 6-ti kolovém mobilním robotu. Podle mých znalostí do této doby neexistoval dataset, který by kromě dat potřebných k řešení úlohy SLAM, naíc přidával data umožňující detekci a klasifikaci prostředí i pomocí nevizuálních dat; (8) Implementace představného systému jako open-source balík pro Robot Operating System na platformě GitHub; (9) Implementace knihovny pro výpočet globálního popisovače Centrist v C++, taktéž dostupná jako open-source na platformě GitHub.ObhájenoThis dissertation thesis deals with the problem of environment changes in the tasks of mobile robotics. In particular, it focuses on using of one-dimensional non-visual sensors in order to reduce computation cost. The work presents a new system for detection and classification of the robot environment based on data from the camera and non-visual sensors. Non-visual sensors serve as detectors of ongoing change of the environment that initiates the classification of the environment using camera data. This can significantly reduce computational demands compared to a situation where every or every n-th frame of an image is processed. The system is evaluated on the case of a change of environment between indoor and outdoor environment. The contributions of this work are the following: (1) Proposed system for detection and classification of the environment of mobile robot; (2) State-of-the-art analysis in the field of Simultaneous Localization and Mapping in order to identify existing open issues that need to be addressed; (3) Analysis of non-visual sensors with respect to their suitability for solving change detection problem. (4) Analysis of existing methods for detecting changes in 2D signal and introduction of two simple approaches to this problem; (5) State-of-the-art analysis in the field of environment classification with a focus on the classification of indoor vs. outdoor environments; (6) Experiment comparing the methods studied in the previous point. To my best knowledge, this is the most extensive comparison of these methods on a single dataset. In addition, classifiers based on neural networks, which achieve better results than classical approaches, are also included in the experiment. (7) Creation of a dataset for testing the designed system on an assembled 6-wheel mobile robot. To the best of my knowledge, there has been no dataset that, in addition to the data needed to solve the SLAM task, adds data that allows the environment to be detected and classified using non-visual data. (8) Implementation of the proposed system as an open-source package for the Robot Operating System on the GitHub platform. (9) Implementation of a library for calculating the Centrist global descriptor in C++ and Python. Library is also available as open-source on the GitHub platform